利用贝叶斯线性回归结合混合Copula函数分析风电功率的相关性

分析风电场之间的出力相关性有助于合理规划风机的功率输送以及调度优化,从而提高传输线路的利用率。以冀北地区风场为例,首先分析了该区域风速的分布特性,然后利用贝叶斯线性回归算法建立混合Copula函数模型,拟合得到4个机群风速序列的联合分布,计算出风电场之间的出力相关性,并与其他相关性函数建模进行对比研究。研究结果表明,基于贝叶斯线性回归的混合Copula函数模型能够提高参数估计的精确性,从而使得计算出的相关性更为准确,并且由其拟合得到的出力概率分布与实际风场出力的概率分布较为一致。     

Bayesian decision-based pattern recognition on spectrum signal of metal transfer modes

A software system of pattern recognition on spectrum signal of metal transfer mode has been developed using Visual Basic under Windows environment. On the basis of the coincidence relation between the spectrum signal and metal transfer mode, according to the geometrical pattern feature of the spectrum signal, several key characteristic parameters are extracted. The correspondent recognition function and a minimum distance classifier have been constructed based on Bayesian decision theory. The results show that using this system, the metal transfer mode of MIG, MAG, CO2 welding can be recognized automatically which provides the basis for automatically controlling of the metal gas arc welding metal transfer.     

矿业投资风险决策的贝叶斯网络方法

针对矿业投资风险决策过程中风险因素随着不断更新的信息而动态变化的特征,分析了传统方法的不足,引入贝叶斯网络分析方法,介绍了贝叶斯网络的基本概念和性质,将风险决策理论与贝叶斯网络有机的结合起来,用于矿业投资风险决策的分析.构建了基于贝叶斯网络的矿山投资风险决策模型,并通过实例证明了该方法的有效性,为矿业投资决策分析提供了一种新的思路和方法.     

Variance analysis of identified linear MISO models having spatially correlated inputs,with application to parallel Hammerstein models

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.     

The effects of individual differences,prior experience and cognitive load on the transfer of dynamic decision-making performance

Situational awareness is recognised as an important factor in the performance of individuals and teams in dynamic decision-making (DDM) environments (Salmon et al. 2014 Salmon, P. M., N. A.Stanton, G. H.Walker, D.Jenkins, D.Ladva, L.Rafferty, and M.Young. 2014. “Measuring SA in Complex Systems: Comparison of Measurement Study.” International Journal of Industrial Ergonomics39: 490–500.[Crossref], [Web of Science ®] , [Google Scholar]). The present study was designed to investigate whether the scores on the WOMBAT? Situational Awareness and Stress Tolerance Test (Roscoe and North 1980 Roscoe, S. N., and R.North. 1980. “Prediction of Pilot Performance.” In Aviation Psychology, edited by S. N.Roscoe, 123–127. Ames: The Iowa State University Press. [Google Scholar]) would predict the transfer of DDM performance from training under different levels of cognitive load to a novel situation. Participants practised a simulated firefighting task under either low or high conditions of cognitive load and then performed a (transfer) test in an alternative firefighting environment under an intermediate level of cognitive load. WOMBAT? test scores were a better predictor of DDM performance than scores on the Raven Matrices. Participants with high WOMBAT? scores performed better regardless of their training condition. Participants with recent gaming experience who practised under low cognitive load showed better practice phase performance but worse transfer performance than those who practised under high cognitive load.

Practitioner Summary: The relationship between task experience, situational awareness ability, cognitive load and the transfer of dynamic decision-making (DDM) performance was investigated. Results showed that the WOMBAT? test predicted transfer of DDM performance regardless of task cognitive load. The effects of cognitive load on performance varied according to previous task-relevant experience.     

基于动态朴素贝叶斯分类器的明渠水华风险评估模型

水华风险不仅是水利工程规划时需要考虑的环境问题,也是水利设施运营时不能忽视的监测项目。为了提高明渠水化风险等级预测的准确率,针对水华成因的不确定性和发展的时序性,基于动态朴素贝叶斯网络分类器提出一种应用于明渠的水华风险评估模型。模型用水华风险等级结点对应藻叶绿素a(Chla)的浓度,并考虑了9项影响水藻生长的因素。采用主成分分析法,处理专家咨询结果,进行参数的设计。在苏州河道北门桥2011年6月初至9月初观测的53例连续监测数据上,与基于朴素贝叶斯网络分类器的评估模型进行比较实验。混淆矩阵显示对中等风险情况的预测识别率提高了15.625%,单尾配对t检验表明在显著性水平0.05时,两模型预测识别率差异显著。考虑了时序特征的基于动态贝叶斯网络分类器的评估模型对明渠中等水化风险的预测识别率提高显著。     

基于Bayes特征判别的选煤建模与识别

研究了煤块的Harr小波变换和幅度投影分析,在对原始图像进行特征判别分析的基础上,建立了煤块和煤矸石的统计模型,提出了用Bayes判别函数分选煤块和煤矸石的算法,实现了用计算机视觉系统对样本进行了识别检验.并运用神经网络BP算法进行训练识别,检验其正确性,最后通过实验验证了算法有效性.     

Bayesian bivariate generalized Lindley model for survival data with a cure fraction

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie–Gumbel–Morgenstern (FGM), Clayton and Gumbel–Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.     

基于智能决策的火箭安控系统

火箭在飞行过程中经常发生各种故障,因此需要对其飞行状态进行监视、判断,及时有效地做出相应的安全控制决策。在分析原有火箭安控系统的基础上,针对火箭飞行的特点及不确定的规则知识,提出了基于智能决策的火箭安控系统。首先给出了集弹道数据处理和安全控制决策于一体的计算机决策支持系统模型;然后根据安控系统的特点,对安控知识进行分类,给出了安控系统知识不确定的表示方法,形成了相应的安控决策网络;最后以Visual C++6.0为开发平台,构建了安控推理决策模型,给出相应的推理流程。仿真实验表示,该方法能实时监测出火箭的飞行状态,对其进行有效的管理,为安控领域专家的最终决策提供支持。     

Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach

In recent years, autonomous robots have increasingly been deployed in unknown environments and required to manipulate or categorize unknown objects. In order to cope with these unfamiliar situations, improvements must be made both in sensing technologies and in the capability to autonomously train perception models. In this paper, we explore this problem in the context of tactile surface identification and categorization. Using a highly-discriminant tactile probe based upon large bandwidth, triple axis accelerometer that is sensitive to surface texture and material properties, we demonstrate that unsupervised learning for surface identification with this tactile probe is feasible. To this end, we derived a Bayesian nonparametric approach based on Pitman–Yor processes to model power-law distributions, an extension of our previous work using Dirichlet processes Dallaire et al. (2011). When tested against a large collection of surfaces and without providing the actual number of surfaces, the tactile probe combined with our proposed approach demonstrated near-perfect recognition in many cases and achieved perfect recognition given the right conditions. We consider that our combined improvements demonstrate the feasibility of effective autonomous tactile perception systems.